Evidence map›Paper›PMID 42267743›Full record

ArticleAnalytical chemistry2026

Label-Free and High-Throughput Quantification of Nanoparticle-Cell Interactions at the Single-Cell Level with Flow Cytometry.

Mobina Mohammadnejad, Majood Haddad, Alex N Frickenstein, Arianna Dambold, Vinit Sheth, Jezan Alexandre, Nathan Means, James Bowman, Kavita Belligund, Hunter Moss and 3 more

Abstract read
In one paragraph

Article in Analytical chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Mobina MohammadnejadStephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States.
Majood HaddadStephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States.
Alex N FrickensteinStephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States.
Arianna DamboldStephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States.
Vinit ShethStephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States.
Jezan AlexandreStephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States.
Nathan MeansStephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States.
James BowmanStephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States.
Kavita BelligundStephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States.
Hunter MossStephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States.
Jeesoo ParkStephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States.
Yuxin HeStephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States.
Stefan WilhelmStephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma 73019, United States.ORCID 0000-0003-2167-6221

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding nanoparticle-cell interactions at the single-cell level is essential for designing next-generation nanomedicines. Here, we explored flow cytometry as a single-cell technique for label-free quantification of nanoparticle-cell interactions. We demonstrated the use of conventional flow cytometry-based side-scattering signals to quantify interactions between nanoparticles and individual cells. We corroborated our findings qualitatively with optical super-resolution microscopy and quantitatively with elemental mass spectrometry. Using a broad, multiparameter workflow, we analyzed >10,000 single cells per minute to evaluate how nanoparticle size, composition, surface chemistry, and concentration affect cellular interactions, and we leveraged this approach to quantify nanoparticle uptake kinetics at the single-cell level. We further validated our findings using super-resolution expansion microscopy and single-particle inductively coupled plasma mass spectrometry, and extended the applicability of this workflow to mixed-cell and coculture in vitro cell models. Our demonstrated workflows enable a quantitative understanding of nanoparticle-cell interactions for the rational design of next-generation nanomedicines that are safer, more effective, and more efficient.

Indexed as

Flow CytometryHigh-Throughput Screening AssaysNanoparticlesSingle-Cell AnalysisHumansMass SpectrometryParticle Size

Identifiers

PMID42267743
PMCPMC13295095

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.